Injection molding is a manufacturing process in which a molten material (e.g., melted plastic) is injected under pressure into a mold cavity or cavities. The molten material is held under pressure in the cavity or cavities until has cooled, at which point the material is removed in a solid state, effectively duplicating the cavity of the mold. Various articles of commercial value, such as plastic bottles, toothbrushes, automotive parts, medical devices, children's toys, etc., are made using injection molding techniques. Injection molding is widely used because of its ability to produce complex parts with low-cost and high throughput. However, as the demand for productivity and throughput grows, it is difficult to achieve the desired quality level without significant monitoring and control.
An injection molding process is a discrete-type manufacturing process in that a material is supplied to a mold, which cools the material very quickly. Given the relatively short time frame involved, observation and control of the process typically involves measurements made before and after the process as a way to understand the process.
A typical process tool used in current injection molding can be described by a set of several hundred (or in some instances several thousand) process variables. The variables are generally related to physical parameters of the manufacturing process and/or tools used in the manufacturing process such as the mold. In some cases, of these several thousand variables, several hundred variables will be dynamic (e.g., changing in time during the manufacturing process or between manufacturing processes). The dynamic variables, for example, material temperature, mold temperature, material composition, delivery time, cooling time, relative humidity, delivered power, current, voltage, and temperature change based on, for example, a specific molten material, mold size, or mold geometry, errors and faults that occur during the manufacturing process or changes in parameter values based on use of a particular mold or cavity (e.g., referred to as “drift”).
Process variables are frequently related to yield or response variables. The process variables can be thought of as predictors or indicators of the yield or response variables based on an underlying relationship between the variables. Data indicative of the process and yield variables are measured and stored during a manufacturing process, either for real-time or later analysis.
A discrete-type manufacturing process, e.g., an injection molding process, can be characterized by operating parameters (e.g., variables that an operator can set) and by process parameters (e.g., variables that are observed about the process). Operating parameters can be related to process parameters, though the quantitative relationship relating operating parameters and process parameters can be complicated and not well-understood.
An injection molding system usually includes a number of built-in temperature, pressure, speed, and stroke sensors to guide the interpretation, adjustment, and/or determination of operating and process parameters of the machine by experienced molding personnel. Not only are such sensors costly, but the relationship between machine operating parameters, process parameters, and the properties, qualities, and characteristics of the molded product are complex and often vary on a case-by-case basis.
While some control technology currently exists including open-loop controllers, limited closed-loop controllers, and certain models based on differential equations or object-oriented hierarchical modeling, such controllers typically control each operating parameter independently of other operating parameters, which essentially ignores the root cause of the process deviations. Such an approach does not easily adjust to slow-moving process changes and instead attempts to provide constant within-run process adjustments. This type of continuous tweaking of the process while it is occurring often leads to further and more serious problems and a seemingly never-ending pursuit of the optimal process.